As shown in Figure REF , the ECAPA-TDNN [1]} model architecture is based on the popular x-vector topology [2]} and it introduces several enhancements to create more robust speaker embeddings. The pooling layer uses a channel- and context-dependent attention mechanism, which allows the network to attend different frames per channel. 1-dimensional Squeeze-Excitation (SE) [3]} blocks rescale the channels of the intermediate frame-level feature maps to insert global context information in the locally operating convolutional blocks. Next, the integration of 1-dimensional Res2-blocks [4]} improves performance while simultaneously reducing the total parameter count by using grouped convolutions in a hierarchical way. Finally, Multi-layer Feature Aggregation (MFA) [5]} merges complementary information before the statistics pooling by concatenating the final frame-level feature map with intermediate feature maps of preceding layers.
